On Mon, Jul 18, 2016 at 10:31 AM, Benjamin Kim <bbuil...@gmail.com> wrote:
> Todd, > > Thanks for the info. I was going to upgrade after the testing, but now, it > looks like I will have to do it earlier than expected. > > I will do the upgrade, then resume. > OK, sounds good. The upgrade shouldn't invalidate any performance testing or anything -- just fixes this important bug. -Todd > On Jul 18, 2016, at 10:29 AM, Todd Lipcon <t...@cloudera.com> wrote: > > Hi Ben, > > Any chance that you are running Kudu 0.9.0 instead of 0.9.1? There's a > known serious bug in 0.9.0 which can cause this kind of corruption. > > Assuming that you are running with replication count 3 this time, you > should be able to move aside that tablet metadata file and start the > server. It will recreate a new repaired replica automatically. > > -Todd > > On Mon, Jul 18, 2016 at 10:28 AM, Benjamin Kim <bbuil...@gmail.com> wrote: > >> During my re-population of the Kudu table, I am getting this error trying >> to restart a tablet server after it went down. The job that populates this >> table has been running for over a week. >> >> [libprotobuf ERROR google/protobuf/message_lite.cc:123] Can't parse >> message of type "kudu.tablet.TabletSuperBlockPB" because it is missing >> required fields: rowsets[2324].columns[15].block >> F0718 17:01:26.783571 468 tablet_server_main.cc:55] Check failed: >> _s.ok() Bad status: IO error: Could not init Tablet Manager: Failed to open >> tablet metadata for tablet: 24637ee6f3e5440181ce3f20b1b298ba: Failed to >> load tablet metadata for tablet id 24637ee6f3e5440181ce3f20b1b298ba: Could >> not load tablet metadata from >> /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba: Unable >> to parse PB from path: >> /mnt/data1/kudu/data/tablet-meta/24637ee6f3e5440181ce3f20b1b298ba >> *** Check failure stack trace: *** >> @ 0x7d794d google::LogMessage::Fail() >> @ 0x7d984d google::LogMessage::SendToLog() >> @ 0x7d7489 google::LogMessage::Flush() >> @ 0x7da2ef google::LogMessageFatal::~LogMessageFatal() >> @ 0x78172b (unknown) >> @ 0x344d41ed5d (unknown) >> @ 0x7811d1 (unknown) >> >> Does anyone know what this means? >> >> Thanks, >> Ben >> >> >> On Jul 11, 2016, at 10:47 AM, Todd Lipcon <t...@cloudera.com> wrote: >> >> On Mon, Jul 11, 2016 at 10:40 AM, Benjamin Kim <bbuil...@gmail.com> >> wrote: >> >>> Todd, >>> >>> I had it at one replica. Do I have to recreate? >>> >> >> We don't currently have the ability to "accept data loss" on a tablet (or >> set of tablets). If the machine is gone for good, then currently the only >> easy way to recover is to recreate the table. If this sounds really >> painful, though, maybe we can work up some kind of tool you could use to >> just recreate the missing tablets (with those rows lost). >> >> -Todd >> >>> >>> On Jul 11, 2016, at 10:37 AM, Todd Lipcon <t...@cloudera.com> wrote: >>> >>> Hey Ben, >>> >>> Is the table that you're querying replicated? Or was it created with >>> only one replica per tablet? >>> >>> -Todd >>> >>> On Mon, Jul 11, 2016 at 10:35 AM, Benjamin Kim <b...@amobee.com> wrote: >>> >>>> Over the weekend, a tablet server went down. It’s not coming back up. >>>> So, I decommissioned it and removed it from the cluster. Then, I restarted >>>> Kudu because I was getting a timeout exception trying to do counts on the >>>> table. Now, when I try again. I get the same error. >>>> >>>> 16/07/11 17:32:36 WARN scheduler.TaskSetManager: Lost task 468.3 in >>>> stage 0.0 (TID 603, prod-dc1-datanode167.pdc1i.gradientx.com): >>>> com.stumbleupon.async.TimeoutException: Timed out after 30000ms when >>>> joining Deferred@712342716(state=PAUSED, result=Deferred@1765902299, >>>> callback=passthrough -> scanner opened -> wakeup thread Executor task >>>> launch worker-2, errback=openScanner errback -> passthrough -> wakeup >>>> thread Executor task launch worker-2) >>>> at com.stumbleupon.async.Deferred.doJoin(Deferred.java:1177) >>>> at com.stumbleupon.async.Deferred.join(Deferred.java:1045) >>>> at org.kududb.client.KuduScanner.nextRows(KuduScanner.java:57) >>>> at >>>> org.kududb.spark.kudu.RowResultIteratorScala.hasNext(KuduRDD.scala:99) >>>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) >>>> at >>>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:88) >>>> at >>>> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:86) >>>> at >>>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>>> at >>>> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>> at >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) >>>> at >>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) >>>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>> at java.lang.Thread.run(Thread.java:745) >>>> >>>> Does anyone know how to recover from this? >>>> >>>> Thanks, >>>> *Benjamin Kim* >>>> *Data Solutions Architect* >>>> >>>> [a•mo•bee] *(n.)* the company defining digital marketing. >>>> >>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>* >>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | >>>> www.amobee.com >>>> >>>> On Jul 6, 2016, at 9:46 AM, Dan Burkert <d...@cloudera.com> wrote: >>>> >>>> >>>> >>>> On Wed, Jul 6, 2016 at 7:05 AM, Benjamin Kim <bbuil...@gmail.com> >>>> wrote: >>>> >>>>> Over the weekend, the row count is up to <500M. I will give it another >>>>> few days to get to 1B rows. I still get consistent times ~15s for doing >>>>> row >>>>> counts despite the amount of data growing. >>>>> >>>>> On another note, I got a solicitation email from SnappyData to >>>>> evaluate their product. They claim to be the “Spark Data Store” with tight >>>>> integration with Spark executors. It claims to be an OLTP and OLAP system >>>>> with being an in-memory data store first then to disk. After going to >>>>> several Spark events, it would seem that this is the new “hot” area for >>>>> vendors. They all (MemSQL, Redis, Aerospike, Datastax, etc.) claim to be >>>>> the best "Spark Data Store”. I’m wondering if Kudu will become this too? >>>>> With the performance I’ve seen so far, it would seem that it can be a >>>>> contender. All that is needed is a hardened Spark connector package, I >>>>> would think. The next evaluation I will be conducting is to see if >>>>> SnappyData’s claims are valid by doing my own tests. >>>>> >>>> >>>> It's hard to compare Kudu against any other data store without a lot of >>>> analysis and thorough benchmarking, but it is certainly a goal of Kudu to >>>> be a great platform for ingesting and analyzing data through Spark. Up >>>> till this point most of the Spark work has been community driven, but more >>>> thorough integration testing of the Spark connector is going to be a focus >>>> going forward. >>>> >>>> - Dan >>>> >>>> >>>> >>>>> Cheers, >>>>> Ben >>>>> >>>>> >>>>> >>>>> On Jun 15, 2016, at 12:47 AM, Todd Lipcon <t...@cloudera.com> wrote: >>>>> >>>>> Hi Benjamin, >>>>> >>>>> What workload are you using for benchmarks? Using spark or something >>>>> more custom? rdd or data frame or SQL, etc? Maybe you can share the schema >>>>> and some queries >>>>> >>>>> Todd >>>>> >>>>> Todd >>>>> On Jun 15, 2016 8:10 AM, "Benjamin Kim" <bbuil...@gmail.com> wrote: >>>>> >>>>>> Hi Todd, >>>>>> >>>>>> Now that Kudu 0.9.0 is out. I have done some tests. Already, I am >>>>>> impressed. Compared to HBase, read and write performance are better. >>>>>> Write >>>>>> performance has the greatest improvement (> 4x), while read is > 1.5x. >>>>>> Albeit, these are only preliminary tests. Do you know of a way to really >>>>>> do >>>>>> some conclusive tests? I want to see if I can match your results on my 50 >>>>>> node cluster. >>>>>> >>>>>> Thanks, >>>>>> Ben >>>>>> >>>>>> On May 30, 2016, at 10:33 AM, Todd Lipcon <t...@cloudera.com> wrote: >>>>>> >>>>>> On Sat, May 28, 2016 at 7:12 AM, Benjamin Kim <bbuil...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> Todd, >>>>>>> >>>>>>> It sounds like Kudu can possibly top or match those numbers put out >>>>>>> by Aerospike. Do you have any performance statistics published or any >>>>>>> instructions as to measure them myself as good way to test? In addition, >>>>>>> this will be a test using Spark, so should I wait for Kudu version 0.9.0 >>>>>>> where support will be built in? >>>>>>> >>>>>> >>>>>> We don't have a lot of benchmarks published yet, especially on the >>>>>> write side. I've found that thorough cross-system benchmarks are very >>>>>> difficult to do fairly and accurately, and often times users end up >>>>>> misguided if they pay too much attention to them :) So, given a finite >>>>>> number of developers working on Kudu, I think we've tended to spend more >>>>>> time on the project itself and less time focusing on "competition". I'm >>>>>> sure there are use cases where Kudu will beat out Aerospike, and probably >>>>>> use cases where Aerospike will beat Kudu as well. >>>>>> >>>>>> From my perspective, it would be great if you can share some details >>>>>> of your workload, especially if there are some areas you're finding Kudu >>>>>> lacking. Maybe we can spot some easy code changes we could make to >>>>>> improve >>>>>> performance, or suggest a tuning variable you could change. >>>>>> >>>>>> -Todd >>>>>> >>>>>> >>>>>>> On May 27, 2016, at 9:19 PM, Todd Lipcon <t...@cloudera.com> wrote: >>>>>>> >>>>>>> On Fri, May 27, 2016 at 8:20 PM, Benjamin Kim <bbuil...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi Mike, >>>>>>>> >>>>>>>> First of all, thanks for the link. It looks like an interesting >>>>>>>> read. I checked that Aerospike is currently at version 3.8.2.3, and in >>>>>>>> the >>>>>>>> article, they are evaluating version 3.5.4. The main thing that >>>>>>>> impressed >>>>>>>> me was their claim that they can beat Cassandra and HBase by 8x for >>>>>>>> writing >>>>>>>> and 25x for reading. Their big claim to fame is that Aerospike can >>>>>>>> write 1M >>>>>>>> records per second with only 50 nodes. I wanted to see if this is real. >>>>>>>> >>>>>>> >>>>>>> 1M records per second on 50 nodes is pretty doable by Kudu as well, >>>>>>> depending on the size of your records and the insertion order. I've been >>>>>>> playing with a ~70 node cluster recently and seen 1M+ writes/second >>>>>>> sustained, and bursting above 4M. These are 1KB rows with 11 columns, >>>>>>> and >>>>>>> with pretty old HDD-only nodes. I think newer flash-based nodes could do >>>>>>> better. >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> To answer your questions, we have a DMP with user profiles with >>>>>>>> many attributes. We create segmentation information off of these >>>>>>>> attributes >>>>>>>> to classify them. Then, we can target advertising appropriately for our >>>>>>>> sales department. Much of the data processing is for applying models >>>>>>>> on all >>>>>>>> or if not most of every profile’s attributes to find similarities >>>>>>>> (nearest >>>>>>>> neighbor/clustering) over a large number of rows when batch processing >>>>>>>> or a >>>>>>>> small subset of rows for quick online scoring. So, our use case is a >>>>>>>> typical advanced analytics scenario. We have tried HBase, but it >>>>>>>> doesn’t >>>>>>>> work well for these types of analytics. >>>>>>>> >>>>>>>> I read, that Aerospike in the release notes, they did do many >>>>>>>> improvements for batch and scan operations. >>>>>>>> >>>>>>>> I wonder what your thoughts are for using Kudu for this. >>>>>>>> >>>>>>> >>>>>>> Sounds like a good Kudu use case to me. I've heard great things >>>>>>> about Aerospike for the low latency random access portion, but I've also >>>>>>> heard that it's _very_ expensive, and not particularly suited to the >>>>>>> columnar scan workload. Lastly, I think the Apache license of Kudu is >>>>>>> much >>>>>>> more appealing than the AGPL3 used by Aerospike. But, that's not really >>>>>>> a >>>>>>> direct answer to the performance question :) >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> Thanks, >>>>>>>> Ben >>>>>>>> >>>>>>>> >>>>>>>> On May 27, 2016, at 6:21 PM, Mike Percy <mpe...@cloudera.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>> Have you considered whether you have a scan heavy or a random >>>>>>>> access heavy workload? Have you considered whether you always access / >>>>>>>> update a whole row vs only a partial row? Kudu is a column store so has >>>>>>>> some awesome performance characteristics when you are doing a lot of >>>>>>>> scanning of just a couple of columns. >>>>>>>> >>>>>>>> I don't know the answer to your question but if your concern is >>>>>>>> performance then I would be interested in seeing comparisons from a >>>>>>>> perf >>>>>>>> perspective on certain workloads. >>>>>>>> >>>>>>>> Finally, a year ago Aerospike did quite poorly in a Jepsen test: >>>>>>>> https://aphyr.com/posts/324-jepsen-aerospike >>>>>>>> >>>>>>>> I wonder if they have addressed any of those issues. >>>>>>>> >>>>>>>> Mike >>>>>>>> >>>>>>>> On Friday, May 27, 2016, Benjamin Kim <bbuil...@gmail.com> wrote: >>>>>>>> >>>>>>>>> I am just curious. How will Kudu compare with Aerospike ( >>>>>>>>> http://www.aerospike.com)? I went to a Spark Roadshow and found >>>>>>>>> out about this piece of software. It appears to fit our use case >>>>>>>>> perfectly >>>>>>>>> since we are an ad-tech company trying to leverage our user profiles >>>>>>>>> data. >>>>>>>>> Plus, it already has a Spark connector and has a SQL-like client. The >>>>>>>>> tables can be accessed using Spark SQL DataFrames and, also, made >>>>>>>>> into SQL >>>>>>>>> tables for direct use with Spark SQL ODBC/JDBC Thriftserver. I see >>>>>>>>> from the >>>>>>>>> work done here http://gerrit.cloudera.org:8080/#/c/2992/ that the >>>>>>>>> Spark integration is well underway and, from the looks of it lately, >>>>>>>>> almost >>>>>>>>> complete. I would prefer to use Kudu since we are already a Cloudera >>>>>>>>> shop, >>>>>>>>> and Kudu is easy to deploy and configure using Cloudera Manager. I >>>>>>>>> also >>>>>>>>> hope that some of Aerospike’s speed optimization techniques can make >>>>>>>>> it >>>>>>>>> into Kudu in the future, if they have not been already thought of or >>>>>>>>> included. >>>>>>>>> >>>>>>>>> Just some thoughts… >>>>>>>>> >>>>>>>>> Cheers, >>>>>>>>> Ben >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> -- >>>>>>>> Mike Percy >>>>>>>> Software Engineer, Cloudera >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Todd Lipcon >>>>>>> Software Engineer, Cloudera >>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Todd Lipcon >>>>>> Software Engineer, Cloudera >>>>>> >>>>>> >>>>>> >>>>> >>>> >>>> >>> >>> >>> -- >>> Todd Lipcon >>> Software Engineer, Cloudera >>> >>> >>> >> >> >> -- >> Todd Lipcon >> Software Engineer, Cloudera >> >> >> > > > -- > Todd Lipcon > Software Engineer, Cloudera > > > -- Todd Lipcon Software Engineer, Cloudera
